Mr. Edward T. Dougherty Profile

Edward T. Dougherty

at Charles River Analytics Inc

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Proceedings Article | 10 May 2006 Paper

Adaptive cyber-attack modeling system

Paul Gonsalves, Edward Dougherty

Proceedings Volume 6201, 620104 (2006) https://doi.org/10.1117/12.666236

KEYWORDS: Systems modeling, Expectation maximization algorithms, Network security, Reverse modeling, Data modeling, Samarium, Information security, Process modeling, Knowledge acquisition, Prototyping

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The pervasiveness of software and networked information systems is evident across a broad spectrum of business and government sectors. Such reliance provides an ample opportunity not only for the nefarious exploits of lone wolf computer hackers, but for more systematic software attacks from organized entities. Much effort and focus has been placed on preventing and ameliorating network and OS attacks, a concomitant emphasis is required to address protection of mission critical software. Typical software protection technique and methodology evaluation and verification and validation (V&V) involves the use of a team of subject matter experts (SMEs) to mimic potential attackers or hackers. This manpower intensive, time-consuming, and potentially cost-prohibitive approach is not amenable to performing the necessary multiple non-subjective analyses required to support quantifying software protection levels. To facilitate the evaluation and V&V of software protection solutions, we have designed and developed a prototype adaptive cyber attack modeling system. Our approach integrates an off-line mechanism for rapid construction of Bayesian belief network (BN) attack models with an on-line model instantiation, adaptation and knowledge acquisition scheme. Off-line model construction is supported via a knowledge elicitation approach for identifying key domain requirements and a process for translating these requirements into a library of BN-based cyber-attack models. On-line attack modeling and knowledge acquisition is supported via BN evidence propagation and model parameter learning.

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